library(plotly)
library(dplyr)
library(lubridate)
This is an exploratory data notebook. It’s a sort of sandbox to
prepare and do some quick visualizations prior to building the Shiny
App.
Load data
Some data was already prepped for this, so we’ll start by loading the
file and doing some preliminary plotting.
indices_table <- read.csv("data/prepped/keywest-withfish.csv")
indices_table <- indices_table %>%
mutate(datetime = ymd_hms(datetime))
# indices_table$datetime <- as.POSIXct(indices_table$datetime, format = "%Y-%m-%d %H:%M:%S")
str(indices_table$datetime)
POSIXct[1:261], format: "2020-02-01 00:05:00" "2020-02-01 00:10:30" "2020-02-01 00:16:00" "2020-02-01 00:21:30" ...
Quick line plot
fig <- plot_ly(data = indices_table, height = 400)
# Adding the line trace
fig <- fig %>%
add_trace(
x = ~datetime,
y = ~ACI,
type = 'scatter',
mode = 'lines',
name = 'ACI'
)
# Adding the marker trace
fig <- fig %>%
add_trace(
x = ~datetime,
y = ~ACI,
type = 'scatter',
mode = 'markers',
name = 'Presence',
marker = list(size = ~Em * 7)
)
# Updating layout
fig <- fig %>%
layout(
legend = list(itemclick = FALSE,
itemdoubleclick = "toggleothers"
#itemsizing = 'constant'
)
)
# Show the plot
fig
NA
Correlation matrix
# Select only the acoustic index columns
index_columns <- names(indices_table)[3:62]
# Calculate the correlation matrix
cor_matrix <- cor(mtcars[cols])
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